Treatment of in‐transit melanoma with intra‐lesional interleukin‐2: A systematic review
Bibliographic record
Abstract
BACKGROUND: Several phase II studies have assessed intra-lesional interleukin-2 (IL-2) for the treatment of in-transit melanoma. This systematic review addresses the efficacy and side effect profile of IL-2. METHODS: MEDLINE, EMBASE, Cochrane Library, and Google Scholar databases were searched from 1980 to 2012 for studies evaluating the clinical response to IL-2 for in-transit melanoma. Titles and abstracts were screened by two independent researchers for suitability using predetermined inclusion and exclusion criteria. A modified quality assessment tool for observational studies was used. Data were pooled and analyzed to determine lesion and patient response rates. RESULTS: Forty-nine studies were identified. Forty-three did not meet inclusion criteria, leaving six observational trials. Heterogeneity was seen in IL-2 dosage and treatment interval. Response rate was variable as well. Overall, 2,182 lesions and 140 patients were treated in these six studies. Pooling the lesions, complete response was seen in 78%. Pooling subjects, 50% achieved a complete response. Treatment was generally well tolerated, with localized pain and swelling, and mild flu-like symptoms. There were only three grade 3 adverse events reported, including rigors, headache, and fever with arthralgia. CONCLUSIONS: Intra-lesional IL-2 safely and effectively provides locoregional control of in-transit melanoma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".